A Mathematical Framework of Multi-source Transfer Learning
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This talk introduces a theoretical framework for multi-source transferability covering distance, sample sizes, and task complexity at the same time. It extends to continuous data via training neural networks. Futhermore, a consistent algorithm is proposed that works in applications.